Overview

Dataset statistics

Number of variables25
Number of observations77414
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.4 MiB
Average record size in memory235.3 B

Variable types

Numeric14
Categorical4
DateTime4
Unsupported3

Alerts

VALOR_A_PAGAR is highly overall correlated with valor_emprestimo and 1 other fieldsHigh correlation
RENDA_MES_ANTERIOR is highly overall correlated with diff_rendaHigh correlation
meses_desde_cadastro is highly overall correlated with dias_desde_cadastroHigh correlation
dias_desde_cadastro is highly overall correlated with SAFRA_REF and 1 other fieldsHigh correlation
valor_emprestimo is highly overall correlated with VALOR_A_PAGAR and 1 other fieldsHigh correlation
diff_renda is highly overall correlated with VALOR_A_PAGAR and 2 other fieldsHigh correlation
tempo_sem_pagar is highly overall correlated with prazo_em_diasHigh correlation
inadimplente is highly overall correlated with tempo_sem_pagarHigh correlation
prazo_em_dias is highly overall correlated with tempo_sem_pagarHigh correlation
SAFRA_REF is highly overall correlated with NO_FUNCIONARIOS and 1 other fieldsHigh correlation
DDD is highly overall correlated with CEP_2_DIGHigh correlation
CEP_2_DIG is highly overall correlated with DDDHigh correlation
NO_FUNCIONARIOS is highly overall correlated with SAFRA_REFHigh correlation
tempo_sem_pagar is highly skewed (γ1 = -45.01485734)Skewed
prazo_em_dias is highly skewed (γ1 = 43.88720817)Skewed
SEGMENTO_INDUSTRIAL is an unsupported type, check if it needs cleaning or further analysisUnsupported
DOMINIO_EMAIL is an unsupported type, check if it needs cleaning or further analysisUnsupported
PORTE is an unsupported type, check if it needs cleaning or further analysisUnsupported
DDD has 7583 (9.8%) zerosZeros
RENDA_MES_ANTERIOR has 1570 (2.0%) zerosZeros
NO_FUNCIONARIOS has 1767 (2.3%) zerosZeros
tempo_sem_pagar has 60742 (78.5%) zerosZeros
ultima_data_emprestimo has 9650 (12.5%) zerosZeros

Reproduction

Analysis started2022-12-16 15:51:10.456432
Analysis finished2022-12-16 15:51:58.246011
Duration47.79 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

ID_CLIENTE
Real number (ℝ)

Distinct1248
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6622701 × 1018
Minimum8.7842371 × 1015
Maximum9.2060308 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-12-16T12:51:58.408986image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum8.7842371 × 1015
5-th percentile4.5929061 × 1017
Q12.3693649 × 1018
median4.817817 × 1018
Q36.9693486 × 1018
95-th percentile8.6099387 × 1018
Maximum9.2060308 × 1018
Range9.1972466 × 1018
Interquartile range (IQR)4.5999837 × 1018

Descriptive statistics

Standard deviation2.6657194 × 1018
Coefficient of variation (CV)0.57176425
Kurtosis-1.2274273
Mean4.6622701 × 1018
Median Absolute Deviation (MAD)2.3245111 × 1018
Skewness-0.080216523
Sum-4.0133142 × 1018
Variance7.1060599 × 1036
MonotonicityNot monotonic
2022-12-16T12:51:58.614986image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.96410875 × 10181151
 
1.5%
5.761480994 × 10181055
 
1.4%
4.008627435 × 1018877
 
1.1%
8.173830875 × 1018675
 
0.9%
6.916556752 × 1018638
 
0.8%
7.930925884 × 1018585
 
0.8%
4.592906068 × 1017557
 
0.7%
4.045739495 × 1018539
 
0.7%
7.325545719 × 1018510
 
0.7%
3.355881108 × 1018505
 
0.7%
Other values (1238) 70322
90.8%
ValueCountFrequency (%)
8.78423715 × 1015241
0.3%
1.507004831 × 10165
 
< 0.1%
1.871961495 × 10167
 
< 0.1%
3.954702544 × 101670
 
0.1%
4.326664122 × 10169
 
< 0.1%
4.963290558 × 101647
 
0.1%
6.62200874 × 101661
 
0.1%
6.97663625 × 101645
 
0.1%
8.611006299 × 101693
 
0.1%
8.643695504 × 10161
 
< 0.1%
ValueCountFrequency (%)
9.20603081 × 1018104
0.1%
9.205015187 × 101826
 
< 0.1%
9.184785003 × 1018121
0.2%
9.175443729 × 10186
 
< 0.1%
9.161263096 × 101836
 
< 0.1%
9.156666134 × 101836
 
< 0.1%
9.142266044 × 10189
 
< 0.1%
9.127318191 × 101857
0.1%
9.108733472 × 101864
0.1%
9.101617111 × 101819
 
< 0.1%

SAFRA_REF
Categorical

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2021-05
 
2531
2021-06
 
2513
2020-11
 
2471
2019-10
 
2464
2019-12
 
2448
Other values (30)
64987 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters541898
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-08
2nd row2018-08
3rd row2018-08
4th row2018-08
5th row2018-08

Common Values

ValueCountFrequency (%)
2021-05 2531
 
3.3%
2021-06 2513
 
3.2%
2020-11 2471
 
3.2%
2019-10 2464
 
3.2%
2019-12 2448
 
3.2%
2020-01 2414
 
3.1%
2020-10 2402
 
3.1%
2021-01 2393
 
3.1%
2020-09 2382
 
3.1%
2019-11 2377
 
3.1%
Other values (25) 53019
68.5%

Length

2022-12-16T12:51:58.762987image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-05 2531
 
3.3%
2021-06 2513
 
3.2%
2020-11 2471
 
3.2%
2019-10 2464
 
3.2%
2019-12 2448
 
3.2%
2020-01 2414
 
3.1%
2020-10 2402
 
3.1%
2021-01 2393
 
3.1%
2020-09 2382
 
3.1%
2019-11 2377
 
3.1%
Other values (25) 53019
68.5%

Most occurring characters

ValueCountFrequency (%)
0 167486
30.9%
2 131314
24.2%
1 85794
15.8%
- 77414
14.3%
9 34166
 
6.3%
8 14793
 
2.7%
6 6796
 
1.3%
5 6752
 
1.2%
3 6557
 
1.2%
4 6196
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 464484
85.7%
Dash Punctuation 77414
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 167486
36.1%
2 131314
28.3%
1 85794
18.5%
9 34166
 
7.4%
8 14793
 
3.2%
6 6796
 
1.5%
5 6752
 
1.5%
3 6557
 
1.4%
4 6196
 
1.3%
7 4630
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 77414
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 541898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 167486
30.9%
2 131314
24.2%
1 85794
15.8%
- 77414
14.3%
9 34166
 
6.3%
8 14793
 
2.7%
6 6796
 
1.3%
5 6752
 
1.2%
3 6557
 
1.2%
4 6196
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 541898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 167486
30.9%
2 131314
24.2%
1 85794
15.8%
- 77414
14.3%
9 34166
 
6.3%
8 14793
 
2.7%
6 6796
 
1.3%
5 6752
 
1.2%
3 6557
 
1.2%
4 6196
 
1.1%
Distinct1040
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Minimum2018-08-17 00:00:00
Maximum2021-06-30 00:00:00
2022-12-16T12:51:58.926987image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:59.095987image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct921
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Minimum2018-06-19 00:00:00
Maximum2021-11-24 00:00:00
2022-12-16T12:51:59.240987image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:59.455757image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct955
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Minimum2017-11-27 00:00:00
Maximum2027-03-31 00:00:00
2022-12-16T12:51:59.615288image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:59.883291image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

VALOR_A_PAGAR
Real number (ℝ)

Distinct68527
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46565.461
Minimum0.1
Maximum4400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-12-16T12:52:00.194291image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile3234.859
Q118752.745
median34751.35
Q360884.205
95-th percentile128377.11
Maximum4400000
Range4399999.9
Interquartile range (IQR)42131.46

Descriptive statistics

Standard deviation46338.921
Coefficient of variation (CV)0.99513503
Kurtosis1153.2635
Mean46565.461
Median Absolute Deviation (MAD)18119.175
Skewness16.283832
Sum3.6048186 × 109
Variance2.1472956 × 109
MonotonicityNot monotonic
2022-12-16T12:52:00.450323image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1182 618
 
0.8%
999 246
 
0.3%
945.6 171
 
0.2%
360 130
 
0.2%
1341 93
 
0.1%
591 73
 
0.1%
1063.8 54
 
0.1%
499 50
 
0.1%
1064 40
 
0.1%
300 31
 
< 0.1%
Other values (68517) 75908
98.1%
ValueCountFrequency (%)
0.1 1
< 0.1%
0.4 1
< 0.1%
0.45 1
< 0.1%
0.7 1
< 0.1%
5.17 1
< 0.1%
5.5 1
< 0.1%
5.78 1
< 0.1%
5.95 1
< 0.1%
6 1
< 0.1%
6.22 1
< 0.1%
ValueCountFrequency (%)
4400000 1
< 0.1%
2250000 1
< 0.1%
1697544.07 1
< 0.1%
1500000 1
< 0.1%
1391835.2 1
< 0.1%
1325000 1
< 0.1%
1210000 1
< 0.1%
1200000 1
< 0.1%
1160000 1
< 0.1%
1000000 1
< 0.1%

TAXA
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
5.99
26459 
6.99
22021 
4.99
15703 
8.99
7934 
11.99
5297 

Length

Max length5
Median length4
Mean length4.0684243
Min length4

Characters and Unicode

Total characters314953
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.99
2nd row6.99
3rd row6.99
4th row6.99
5th row6.99

Common Values

ValueCountFrequency (%)
5.99 26459
34.2%
6.99 22021
28.4%
4.99 15703
20.3%
8.99 7934
 
10.2%
11.99 5297
 
6.8%

Length

2022-12-16T12:52:00.686292image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-16T12:52:01.241275image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
5.99 26459
34.2%
6.99 22021
28.4%
4.99 15703
20.3%
8.99 7934
 
10.2%
11.99 5297
 
6.8%

Most occurring characters

ValueCountFrequency (%)
9 154828
49.2%
. 77414
24.6%
5 26459
 
8.4%
6 22021
 
7.0%
4 15703
 
5.0%
1 10594
 
3.4%
8 7934
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 237539
75.4%
Other Punctuation 77414
 
24.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 154828
65.2%
5 26459
 
11.1%
6 22021
 
9.3%
4 15703
 
6.6%
1 10594
 
4.5%
8 7934
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 77414
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 314953
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 154828
49.2%
. 77414
24.6%
5 26459
 
8.4%
6 22021
 
7.0%
4 15703
 
5.0%
1 10594
 
3.4%
8 7934
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 154828
49.2%
. 77414
24.6%
5 26459
 
8.4%
6 22021
 
7.0%
4 15703
 
5.0%
1 10594
 
3.4%
8 7934
 
2.5%
Distinct732
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Minimum2000-08-15 00:00:00
Maximum2021-06-23 00:00:00
2022-12-16T12:52:01.395276image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:52:01.534278image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DDD
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.859754
Minimum0
Maximum99
Zeros7583
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-12-16T12:52:01.715307image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median33
Q362
95-th percentile91
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation27.760574
Coefficient of variation (CV)0.73324761
Kurtosis-0.8790892
Mean37.859754
Median Absolute Deviation (MAD)22
Skewness0.45143865
Sum2930875
Variance770.64948
MonotonicityNot monotonic
2022-12-16T12:52:01.859279image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 8583
 
11.1%
0 7583
 
9.8%
31 3030
 
3.9%
19 2741
 
3.5%
21 2672
 
3.5%
41 2415
 
3.1%
71 2364
 
3.1%
62 2324
 
3.0%
43 2219
 
2.9%
12 1611
 
2.1%
Other values (65) 41872
54.1%
ValueCountFrequency (%)
0 7583
9.8%
1 389
 
0.5%
2 212
 
0.3%
3 18
 
< 0.1%
4 257
 
0.3%
5 86
 
0.1%
6 300
 
0.4%
7 18
 
< 0.1%
8 191
 
0.2%
9 143
 
0.2%
ValueCountFrequency (%)
99 611
0.8%
98 636
0.8%
95 224
 
0.3%
94 653
0.8%
93 420
0.5%
92 362
 
0.5%
91 1026
1.3%
88 457
0.6%
87 301
 
0.4%
86 519
0.7%

FLAG_PF
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
77195 
1
 
219

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters77414
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 77195
99.7%
1 219
 
0.3%

Length

2022-12-16T12:52:01.978279image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-16T12:52:02.107279image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0 77195
99.7%
1 219
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 77195
99.7%
1 219
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77414
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77195
99.7%
1 219
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 77414
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77195
99.7%
1 219
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77195
99.7%
1 219
 
0.3%

SEGMENTO_INDUSTRIAL
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.2 MiB

DOMINIO_EMAIL
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.2 MiB

PORTE
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.2 MiB

CEP_2_DIG
Real number (ℝ)

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.320653
Minimum0
Maximum99
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-12-16T12:52:02.226280image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q129
median54
Q379
95-th percentile95
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation27.876572
Coefficient of variation (CV)0.52281003
Kurtosis-1.412881
Mean53.320653
Median Absolute Deviation (MAD)25
Skewness0.012713365
Sum4127765
Variance777.10327
MonotonicityNot monotonic
2022-12-16T12:52:02.371918image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 3888
 
5.0%
35 2845
 
3.7%
68 2603
 
3.4%
89 2588
 
3.3%
86 2135
 
2.8%
37 2107
 
2.7%
12 2076
 
2.7%
78 2067
 
2.7%
75 1773
 
2.3%
38 1673
 
2.2%
Other values (80) 53659
69.3%
ValueCountFrequency (%)
0 8
 
< 0.1%
11 914
 
1.2%
12 2076
2.7%
13 3888
5.0%
14 1381
 
1.8%
15 1245
 
1.6%
16 728
 
0.9%
17 751
 
1.0%
18 624
 
0.8%
19 837
 
1.1%
ValueCountFrequency (%)
99 841
1.1%
98 1081
1.4%
97 490
0.6%
96 682
0.9%
95 1181
1.5%
94 213
 
0.3%
93 902
1.2%
92 296
 
0.4%
91 163
 
0.2%
90 498
0.6%

RENDA_MES_ANTERIOR
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct18249
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285130.1
Minimum0
Maximum1682759
Zeros1570
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-12-16T12:52:02.512478image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31618
Q1126902
median235330
Q3391492
95-th percentile703707
Maximum1682759
Range1682759
Interquartile range (IQR)264590

Descriptive statistics

Standard deviation214887.8
Coefficient of variation (CV)0.75364825
Kurtosis2.4978554
Mean285130.1
Median Absolute Deviation (MAD)124555
Skewness1.3428007
Sum2.2073062 × 1010
Variance4.6176767 × 1010
MonotonicityNot monotonic
2022-12-16T12:52:02.678490image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1570
 
2.0%
120152 91
 
0.1%
168672 72
 
0.1%
118073 55
 
0.1%
415262 51
 
0.1%
703707 51
 
0.1%
293852 49
 
0.1%
260432 45
 
0.1%
444595 45
 
0.1%
466768 42
 
0.1%
Other values (18239) 75343
97.3%
ValueCountFrequency (%)
0 1570
2.0%
105 8
 
< 0.1%
154 2
 
< 0.1%
216 7
 
< 0.1%
258 19
 
< 0.1%
352 6
 
< 0.1%
402 2
 
< 0.1%
531 1
 
< 0.1%
549 1
 
< 0.1%
632 4
 
< 0.1%
ValueCountFrequency (%)
1682759 5
< 0.1%
1646635 4
< 0.1%
1634789 5
< 0.1%
1622248 4
< 0.1%
1614315 7
< 0.1%
1613297 3
< 0.1%
1592917 1
 
< 0.1%
1583291 4
< 0.1%
1498812 2
 
< 0.1%
1478508 1
 
< 0.1%

NO_FUNCIONARIOS
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct128
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.21209
Minimum0
Maximum198
Zeros1767
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-12-16T12:52:02.844436image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile84
Q1105
median117
Q3130
95-th percentile147
Maximum198
Range198
Interquartile range (IQR)25

Descriptive statistics

Standard deviation25.020656
Coefficient of variation (CV)0.21717039
Kurtosis8.0513549
Mean115.21209
Median Absolute Deviation (MAD)13
Skewness-2.0341903
Sum8919029
Variance626.03321
MonotonicityNot monotonic
2022-12-16T12:52:02.991610image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117 1859
 
2.4%
116 1837
 
2.4%
122 1836
 
2.4%
120 1790
 
2.3%
0 1767
 
2.3%
111 1691
 
2.2%
121 1689
 
2.2%
118 1627
 
2.1%
112 1613
 
2.1%
125 1607
 
2.1%
Other values (118) 60098
77.6%
ValueCountFrequency (%)
0 1767
2.3%
60 5
 
< 0.1%
61 5
 
< 0.1%
62 1
 
< 0.1%
63 6
 
< 0.1%
64 10
 
< 0.1%
65 4
 
< 0.1%
66 16
 
< 0.1%
67 37
 
< 0.1%
68 7
 
< 0.1%
ValueCountFrequency (%)
198 1
 
< 0.1%
187 2
 
< 0.1%
186 1
 
< 0.1%
185 7
< 0.1%
182 7
< 0.1%
181 6
< 0.1%
180 1
 
< 0.1%
179 4
< 0.1%
178 7
< 0.1%
177 8
< 0.1%

tempo_sem_pagar
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct317
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.17142894
Minimum-2661
Maximum869
Zeros60742
Zeros (%)78.5%
Negative7906
Negative (%)10.2%
Memory size1.2 MiB
2022-12-16T12:52:03.157062image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-2661
5-th percentile-3
Q10
median0
Q30
95-th percentile6
Maximum869
Range3530
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.229477
Coefficient of variation (CV)-147.17163
Kurtosis3650.6049
Mean-0.17142894
Median Absolute Deviation (MAD)0
Skewness-45.014857
Sum-13271
Variance636.5265
MonotonicityNot monotonic
2022-12-16T12:52:03.304062image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60742
78.5%
-1 2841
 
3.7%
1 1898
 
2.5%
5 955
 
1.2%
-3 815
 
1.1%
7 718
 
0.9%
6 700
 
0.9%
2 602
 
0.8%
-2 583
 
0.8%
3 525
 
0.7%
Other values (307) 7035
 
9.1%
ValueCountFrequency (%)
-2661 1
< 0.1%
-2070 1
< 0.1%
-1896 2
< 0.1%
-1303 1
< 0.1%
-1297 1
< 0.1%
-1284 1
< 0.1%
-1229 1
< 0.1%
-1224 1
< 0.1%
-1183 1
< 0.1%
-1105 1
< 0.1%
ValueCountFrequency (%)
869 1
< 0.1%
541 1
< 0.1%
522 1
< 0.1%
458 1
< 0.1%
400 1
< 0.1%
379 1
< 0.1%
370 1
< 0.1%
365 1
< 0.1%
331 1
< 0.1%
329 1
< 0.1%

inadimplente
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
71978 
1
 
5436

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters77414
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 71978
93.0%
1 5436
 
7.0%

Length

2022-12-16T12:52:03.446071image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-16T12:52:03.563080image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0 71978
93.0%
1 5436
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 71978
93.0%
1 5436
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77414
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71978
93.0%
1 5436
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77414
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 71978
93.0%
1 5436
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 71978
93.0%
1 5436
 
7.0%

prazo_em_dias
Real number (ℝ)

HIGH CORRELATION
SKEWED

Distinct244
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.320575
Minimum-420
Maximum2677
Zeros0
Zeros (%)0.0%
Negative27
Negative (%)< 0.1%
Memory size1.2 MiB
2022-12-16T12:52:03.682079image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-420
5-th percentile16
Q116
median18
Q324
95-th percentile45
Maximum2677
Range3097
Interquartile range (IQR)8

Descriptive statistics

Standard deviation26.137018
Coefficient of variation (CV)1.1207707
Kurtosis3168.4273
Mean23.320575
Median Absolute Deviation (MAD)2
Skewness43.887208
Sum1805339
Variance683.1437
MonotonicityNot monotonic
2022-12-16T12:52:03.818080image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 19239
24.9%
18 10131
13.1%
17 9160
11.8%
22 4776
 
6.2%
19 4640
 
6.0%
20 4548
 
5.9%
25 2545
 
3.3%
21 2455
 
3.2%
36 1793
 
2.3%
30 1769
 
2.3%
Other values (234) 16358
21.1%
ValueCountFrequency (%)
-420 1
< 0.1%
-320 1
< 0.1%
-319 1
< 0.1%
-256 1
< 0.1%
-187 1
< 0.1%
-110 2
< 0.1%
-107 2
< 0.1%
-74 1
< 0.1%
-67 1
< 0.1%
-62 1
< 0.1%
ValueCountFrequency (%)
2677 1
< 0.1%
2107 1
< 0.1%
1911 2
< 0.1%
1318 2
< 0.1%
1313 1
< 0.1%
1244 2
< 0.1%
1198 1
< 0.1%
1124 1
< 0.1%
1069 1
< 0.1%
1040 1
< 0.1%

meses_desde_cadastro
Real number (ℝ)

Distinct252
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.88077
Minimum-1
Maximum250
Zeros88
Zeros (%)0.1%
Negative7
Negative (%)< 0.1%
Memory size1.2 MiB
2022-12-16T12:52:03.990081image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile15
Q160
median102
Q3179
95-th percentile243
Maximum250
Range251
Interquartile range (IQR)119

Descriptive statistics

Standard deviation74.58118
Coefficient of variation (CV)0.63268317
Kurtosis-1.0283621
Mean117.88077
Median Absolute Deviation (MAD)49
Skewness0.4547474
Sum9125622
Variance5562.3524
MonotonicityNot monotonic
2022-12-16T12:52:04.430271image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 750
 
1.0%
93 724
 
0.9%
104 721
 
0.9%
95 721
 
0.9%
97 720
 
0.9%
107 702
 
0.9%
99 700
 
0.9%
92 693
 
0.9%
94 692
 
0.9%
101 688
 
0.9%
Other values (242) 70303
90.8%
ValueCountFrequency (%)
-1 7
 
< 0.1%
0 88
 
0.1%
1 163
0.2%
2 175
0.2%
3 196
0.3%
4 227
0.3%
5 253
0.3%
6 199
0.3%
7 237
0.3%
8 274
0.4%
ValueCountFrequency (%)
250 532
0.7%
249 550
0.7%
248 538
0.7%
247 553
0.7%
246 420
0.5%
245 478
0.6%
244 534
0.7%
243 526
0.7%
242 527
0.7%
241 508
0.7%

dias_desde_cadastro
Real number (ℝ)

Distinct455
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3566.1491
Minimum-1
Maximum7663
Zeros88
Zeros (%)0.1%
Negative7
Negative (%)< 0.1%
Memory size1.2 MiB
2022-12-16T12:52:04.577888image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile369
Q11825
median2927
Q35474
95-th percentile7303
Maximum7663
Range7664
Interquartile range (IQR)3649

Descriptive statistics

Standard deviation2291.6023
Coefficient of variation (CV)0.64259855
Kurtosis-1.0082434
Mean3566.1491
Median Absolute Deviation (MAD)1460
Skewness0.49440663
Sum2.7606987 × 108
Variance5251441
MonotonicityNot monotonic
2022-12-16T12:52:04.749900image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2919 710
 
0.9%
3284 702
 
0.9%
2921 693
 
0.9%
2920 660
 
0.9%
2923 648
 
0.8%
6939 642
 
0.8%
2924 628
 
0.8%
2562 627
 
0.8%
3285 621
 
0.8%
6937 621
 
0.8%
Other values (445) 70862
91.5%
ValueCountFrequency (%)
-1 7
 
< 0.1%
0 88
0.1%
1 147
0.2%
2 135
0.2%
3 156
0.2%
4 137
0.2%
5 169
0.2%
6 102
0.1%
7 105
0.1%
8 116
0.1%
ValueCountFrequency (%)
7663 532
0.7%
7662 550
0.7%
7661 538
0.7%
7660 553
0.7%
7659 420
0.5%
7658 478
0.6%
7304 534
0.7%
7303 526
0.7%
7302 527
0.7%
7301 508
0.7%

valor_emprestimo
Real number (ℝ)

Distinct70419
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43626.239
Minimum0.093466679
Maximum4112533.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-12-16T12:52:04.933897image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0.093466679
5-th percentile3021.7229
Q117547.345
median32541.981
Q357088.05
95-th percentile120220.81
Maximum4112533.9
Range4112533.8
Interquartile range (IQR)39540.705

Descriptive statistics

Standard deviation43466.58
Coefficient of variation (CV)0.99634029
Kurtosis1142.5027
Mean43626.239
Median Absolute Deviation (MAD)16954.432
Skewness16.249908
Sum3.3772816 × 109
Variance1.8893435 × 109
MonotonicityNot monotonic
2022-12-16T12:52:05.076898image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1115.199547 195
 
0.3%
1104.776147 192
 
0.2%
1125.821507 127
 
0.2%
933.7321245 80
 
0.1%
942.5417492 73
 
0.1%
892.1596377 65
 
0.1%
1084.503165 54
 
0.1%
883.8209178 52
 
0.1%
1055.45138 50
 
0.1%
951.5191923 47
 
0.1%
Other values (70409) 76479
98.8%
ValueCountFrequency (%)
0.09346667913 1
< 0.1%
0.3738667165 1
< 0.1%
0.4286122488 1
< 0.1%
0.6250558086 1
< 0.1%
4.832227311 1
< 0.1%
5.238594152 1
< 0.1%
5.453344655 1
< 0.1%
5.613737145 1
< 0.1%
5.660911407 1
< 0.1%
5.92437375 1
< 0.1%
ValueCountFrequency (%)
4112533.882 1
< 0.1%
2122841.778 1
< 0.1%
1601607.765 1
< 0.1%
1415227.852 1
< 0.1%
1300902.14 1
< 0.1%
1262024.955 1
< 0.1%
1152490.713 1
< 0.1%
1142965.997 1
< 0.1%
1084213.478 1
< 0.1%
943485.2345 1
< 0.1%

diff_renda
Real number (ℝ)

Distinct72923
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238564.64
Minimum-4017272
Maximum1666948.5
Zeros0
Zeros (%)0.0%
Negative6221
Negative (%)8.0%
Memory size1.2 MiB
2022-12-16T12:52:05.232381image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-4017272
5-th percentile-19583.156
Q180617.702
median192231.27
Q3345820
95-th percentile662991.98
Maximum1666948.5
Range5684220.5
Interquartile range (IQR)265202.3

Descriptive statistics

Standard deviation219497.44
Coefficient of variation (CV)0.92007533
Kurtosis4.5285117
Mean238564.64
Median Absolute Deviation (MAD)127027.79
Skewness1.1264315
Sum1.8468243 × 1010
Variance4.8179126 × 1010
MonotonicityNot monotonic
2022-12-16T12:52:05.364381image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1182 29
 
< 0.1%
-26498.5 13
 
< 0.1%
299159 9
 
< 0.1%
335996.49 9
 
< 0.1%
58693 9
 
< 0.1%
199585.51 8
 
< 0.1%
399700 8
 
< 0.1%
227340.75 8
 
< 0.1%
-945.6 8
 
< 0.1%
234044.49 8
 
< 0.1%
Other values (72913) 77305
99.9%
ValueCountFrequency (%)
-4017272 1
< 0.1%
-2190198 1
< 0.1%
-1619335.07 1
< 0.1%
-1444077 1
< 0.1%
-1267730 1
< 0.1%
-1081481 1
< 0.1%
-991929 1
< 0.1%
-901252 1
< 0.1%
-850889.2 1
< 0.1%
-787125 1
< 0.1%
ValueCountFrequency (%)
1666948.46 1
< 0.1%
1662590.82 1
< 0.1%
1662150.07 1
< 0.1%
1645689.4 1
< 0.1%
1642923.5 1
< 0.1%
1633607 1
< 0.1%
1629676.63 1
< 0.1%
1605036.1 1
< 0.1%
1603360 1
< 0.1%
1602147.59 1
< 0.1%

len_credit_history
Real number (ℝ)

Distinct238
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.55786
Minimum1
Maximum1151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-12-16T12:52:05.524385image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22
Q182
median129
Q3241
95-th percentile638
Maximum1151
Range1150
Interquartile range (IQR)159

Descriptive statistics

Standard deviation220.92867
Coefficient of variation (CV)1.0747761
Kurtosis6.590679
Mean205.55786
Median Absolute Deviation (MAD)65
Skewness2.4746214
Sum15913056
Variance48809.478
MonotonicityNot monotonic
2022-12-16T12:52:05.679380image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1151 1151
 
1.5%
1055 1055
 
1.4%
157 942
 
1.2%
877 877
 
1.1%
130 780
 
1.0%
375 750
 
1.0%
125 750
 
1.0%
124 744
 
1.0%
122 732
 
0.9%
102 714
 
0.9%
Other values (228) 68919
89.0%
ValueCountFrequency (%)
1 108
0.1%
2 128
0.2%
3 180
0.2%
4 220
0.3%
5 195
0.3%
6 204
0.3%
7 203
0.3%
8 192
0.2%
9 171
0.2%
10 150
0.2%
ValueCountFrequency (%)
1151 1151
1.5%
1055 1055
1.4%
877 877
1.1%
675 675
0.9%
638 638
0.8%
585 585
0.8%
557 557
0.7%
539 539
0.7%
510 510
0.7%
505 505
0.7%

ultima_data_emprestimo
Real number (ℝ)

Distinct382
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.556837
Minimum0
Maximum901
Zeros9650
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-12-16T12:52:05.829381image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q311
95-th percentile35
Maximum901
Range901
Interquartile range (IQR)9

Descriptive statistics

Standard deviation26.462226
Coefficient of variation (CV)2.5066434
Kurtosis234.62211
Mean10.556837
Median Absolute Deviation (MAD)3
Skewness12.226621
Sum817247
Variance700.24941
MonotonicityNot monotonic
2022-12-16T12:52:05.980331image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9650
12.5%
1 9532
12.3%
2 7821
 
10.1%
3 6529
 
8.4%
4 5475
 
7.1%
5 4246
 
5.5%
7 3998
 
5.2%
6 3630
 
4.7%
8 2533
 
3.3%
9 2091
 
2.7%
Other values (372) 21909
28.3%
ValueCountFrequency (%)
0 9650
12.5%
1 9532
12.3%
2 7821
10.1%
3 6529
8.4%
4 5475
7.1%
5 4246
5.5%
6 3630
 
4.7%
7 3998
5.2%
8 2533
 
3.3%
9 2091
 
2.7%
ValueCountFrequency (%)
901 1
< 0.1%
846 1
< 0.1%
837 1
< 0.1%
833 1
< 0.1%
808 1
< 0.1%
727 1
< 0.1%
719 2
< 0.1%
714 2
< 0.1%
683 1
< 0.1%
672 1
< 0.1%

Interactions

2022-12-16T12:51:55.246744image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:24.889231image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:27.305670image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:29.439280image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:31.764222image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:33.703668image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:36.619085image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:38.867626image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:41.523751image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:43.787343image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:46.478081image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2022-12-16T12:51:48.683055image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
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Correlations

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Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-16T12:52:06.346331image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-16T12:52:06.594822image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-16T12:52:06.825011image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-16T12:52:07.030039image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-16T12:52:07.162966image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-16T12:51:57.205825image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-16T12:51:57.777044image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ID_CLIENTESAFRA_REFDATA_EMISSAO_DOCUMENTODATA_PAGAMENTODATA_VENCIMENTOVALOR_A_PAGARTAXADATA_CADASTRODDDFLAG_PFSEGMENTO_INDUSTRIALDOMINIO_EMAILPORTECEP_2_DIGRENDA_MES_ANTERIORNO_FUNCIONARIOStempo_sem_pagarinadimplenteprazo_em_diasmeses_desde_cadastrodias_desde_cadastrovalor_emprestimodiff_rendalen_credit_historyultima_data_emprestimo
016612403959032306762018-082018-08-172018-09-062018-09-0635516.416.992013-08-2299.00ServiçosYAHOOPEQUENO65.00.00.0002060182533196.008973-35516.41116.00.0
116612403959032306762018-082018-08-192018-09-112018-09-1017758.216.992013-08-2299.00ServiçosYAHOOPEQUENO65.00.00.0102260182516598.009160-17758.21116.02.0
216612403959032306762018-082018-08-262018-09-182018-09-1717431.966.992013-08-2299.00ServiçosYAHOOPEQUENO65.00.00.0102260182516293.074119-17431.96116.07.0
316612403959032306762018-082018-08-302018-10-112018-10-051341.006.992013-08-2299.00ServiçosYAHOOPEQUENO65.00.00.061366018251253.388167-1341.00116.04.0
416612403959032306762018-082018-08-312018-09-202018-09-2021309.856.992013-08-2299.00ServiçosYAHOOPEQUENO65.00.00.0002060182519917.609122-21309.85116.01.0
582749863284795960382018-082018-08-172018-09-252018-09-2548811.356.992017-01-2531.00ComércioYAHOOMEDIO77.00.00.000391937245622.347883-48811.3543.00.0
63454478884601379012018-082018-08-172018-09-052018-09-0555131.205.992000-08-1575.00ServiçosHOTMAILPEQUENO48.00.00.00019216657052015.473158-55131.2028.00.0
710031448345893721982018-082018-08-172018-09-032018-09-0385855.046.992017-08-0649.00ServiçosOUTLOOKPEQUENO89.00.00.000171236580245.854753-85855.04148.00.0
83249167569722360082018-082018-08-172018-09-032018-09-0342072.005.992011-02-1488.00ServiçosGMAILGRANDE62.00.00.0001790256139694.310784-42072.00252.00.0
93249167569722360082018-082018-08-192018-09-052018-09-0521071.975.992011-02-1488.00ServiçosGMAILGRANDE62.00.00.0001790256119881.092556-21071.97252.02.0
ID_CLIENTESAFRA_REFDATA_EMISSAO_DOCUMENTODATA_PAGAMENTODATA_VENCIMENTOVALOR_A_PAGARTAXADATA_CADASTRODDDFLAG_PFSEGMENTO_INDUSTRIALDOMINIO_EMAILPORTECEP_2_DIGRENDA_MES_ANTERIORNO_FUNCIONARIOStempo_sem_pagarinadimplenteprazo_em_diasmeses_desde_cadastrodias_desde_cadastrovalor_emprestimodiff_rendalen_credit_historyultima_data_emprestimo
774045113050410474448252021-062021-06-302021-07-162021-07-1649318.2011.992011-02-1479.00ServiçosYAHOOGRANDE49.0183354.087.00016124365444038.039111134035.8082.027.0
7740582522157664137812022021-062021-06-302021-08-162021-08-1665599.605.992011-02-1453.00ComércioYAHOOMEDIO96.01178103.0109.00047124365461892.2539861112503.4040.041.0
7740684801095081910861692021-062021-06-302021-07-162021-07-1695487.055.992011-02-1411.00IndústriaGMAILPEQUENO40.0241007.0126.00016124365490090.621757145519.9577.015.0
7740776863612381956909252021-062021-06-302021-07-162021-07-1625979.956.992014-02-0249.00ServiçosAOLPEQUENO89.0445981.0145.0001688255924282.596504420001.05146.017.0
7740845306315573583497112021-062021-06-302021-07-162021-07-1663971.515.992000-08-1511.00ServiçosHOTMAILPEQUENO55.0139142.0116.00016250766360356.17511175170.49125.016.0
7740929515635491977992782021-062021-06-302021-07-162021-07-1689980.005.992000-08-1511.00ComércioAOLPEQUENO13.0280343.0161.00016250766384894.801396190363.00127.031.0
7741052202064083015805912021-062021-06-302021-08-162021-08-1642239.005.992021-04-0819.00IndústriaGMAILGRANDE25.0235315.087.000472239851.872818193076.002.021.0
7741158602763717891404502021-062021-06-302021-07-162021-07-1620921.505.992011-02-1591.00ServiçosHOTMAILGRANDE67.0100006.0126.00016124365419739.12633379084.50161.017.0
7741228147902094365512162021-062021-06-302021-07-162021-07-1690231.056.992021-05-131.00ServiçosYAHOOMEDIO14.00.00.000161184335.965978-90231.051.00.0
7741383439412627922492322021-062021-06-302021-08-162021-08-1620736.514.992019-05-2811.00IndústriaHOTMAILGRANDE31.097599.0116.000472573119750.93818576862.49100.015.0